neuropercolation/src/cellular_automaton/cellular_automaton.py

import multiprocessing

from cellular_automaton.ca_rule import Rule
from cellular_automaton.ca_cell import Cell


class CellularAutomaton:
    def __init__(self, cells, dimension, evolution_rule: Rule):
        self.cells = cells
        self.dimension = dimension
        self.evolution_rule = evolution_rule
        self.evolution_iteration_index = multiprocessing.RawValue('i', -1)


class CellularAutomatonProcessor:
    def __init__(self, cellular_automaton, process_count: int = 1):
        self.ca = cellular_automaton
        cells = {i: (c.state, c.neighbours) for i, c in enumerate(self.ca.cells)}
        self.evolve_range = range(len(self.ca.cells))
        self._evolve_method = lambda x, y: None

        if process_count > 1:
            self.pool = multiprocessing.Pool(processes=process_count,
                                             initializer=_init_process,
                                             initargs=(cells,
                                                       self.ca.evolution_rule,
                                                       self.ca.evolution_iteration_index))
            self._evolve_method = self.pool.map
        else:
            _init_process(cells, self.ca.evolution_rule, self.ca.evolution_iteration_index)
            self._evolve_method = lambda x, y: list(map(x, y))
        for cell in self.ca.cells:
            del cell.neighbours

    def evolve_x_times(self, x):
        for x in range(x):
            self.evolve()

    def evolve(self):
        self.ca.evolution_iteration_index.value += 1
        self._evolve_method(_process_routine, self.evolve_range)


global_cells = None
global_rule = None
global_iteration = None


def _init_process(cells, rule, index):
    global global_rule, global_cells, global_iteration
    global_cells = cells
    global_rule = rule
    global_iteration = index


def _process_routine(i):
    Cell.evolve_if_ready(global_cells[i], global_rule.evolve_cell, global_iteration.value)